Courses Teaching AI and Digital Public Health

Our courses are built on a simple order of operations. The public health question and critical reasoning come first, and the technology arrives afterwards as an instrument to support health equity outcomes. Students learn what AI is and how it works, but they also learn with it, build with it, challenge it, and learn to hold it to account. The sequence runs from general education through graduate level, so a student can enter with no technical background at all and leave able to prototype, evaluate, question, and govern a tool.

Individual courses are one way in. There are now two structured pathways as well.

Degree Pathways

BS Emphasis: AI Innovation in Population Health

Available to declare for Fall 2026. In person Fall 2026, online to be confirmed. Part of the accredited Bachelor of Science in Public Health.

Artificial intelligence is not a distant possibility in public health. Health departments already use machine learning to anticipate outbreaks. Hospitals deploy AI to reduce diagnostic error. International organizations run surveillance platforms that detect epidemics weeks ahead of traditional systems. The people who can stand between population health expertise and AI capability are in short supply and in high demand.

This emphasis exists because the workforce needs professionals who understand both sides. Students do not only learn about AI as a concept. They learn to use it: to analyze health data, to evaluate digital interventions, to work through the ethical questions that come with algorithmic decision-making, and to design strategies that make AI work for real communities, including the ones most often left behind when a new technology arrives.

Graduates leave prepared for a local health department, a technology company with a health mission, a global NGO, or a federal agency working to modernize public health infrastructure.

Required course (3 units)

• GHI 463 — Foundations of Public Health Applications of Artificial Intelligence

Emphasis options (choose 9 additional units)

• EPID 453 — Health Data Science Practice
• HSD 415 — Design Visualization Practices for Health
• HSD 420 — Makerspaces, Design Practices, and Community Impacts
• BSM 441 — Diagnostic Technologies and Their Role in Healthcare
• INFO 402 — Data Ethics
• SOC 301A — Introduction to Computational Social Science
• BIOS 452 — Health Data Analysis and Communication Methods
• PHP 465 — Leadership and Strategy for Population Health Using Generative AI

Where it leads

Students in the major complete 6 units of internship before graduation, and all students declare an emphasis in order to do so. The emphasis assumes moderate comfort with mathematics and carries the second-semester language proficiency requirement that applies across the degree.

See the full emphasis page  How to declare an emphasis · 

For more information, please contact our Office of Student Affairs Undergraduate Advising: coph-undergrad@arizona.edu

Minor in AI and Population Health — coming

A minor is in development, intended for students from any major who want formal grounding in AI applied to population health without taking a full public health degree. It follows the same public health first logic as the rest of our teaching: the domains of public health lead, and AI and machine learning arrive as instruments. Curriculum details and the first term of enrollment will be posted here as soon as they are confirmed.